{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#实验要求：使用加州大学机器学习库的酒数据集(https://archive-beta.ics.uci.edu/dataset/186/wine+quality)包含\n",
    "#了1599种不同红酒的11种物理化学属性，每种酒的质量由真人打分，分数范围从0到10,0代表质量最差，10代表最好\n",
    "#现在希望基于已有酒的物理化学属性来预测未知酒的质量，因此该问题可以看作一个回归问题。\n",
    "#训练数据包含的属性有：非挥发性酸、挥发性酸、柠檬酸、剩余糖分、氯化物、单体硫、总二氧化硫、密度、pH值、\n",
    "#硫酸盐、酒精含量和质量。\n",
    "#可以把质量看成目标变量，其他属性看成自变量进行学习\n",
    "\n",
    "#实验任务：\n",
    "#1、从winequality-red.csv文件中读入输入到一个Pandas对象中，并查看数据的基本情况\n",
    "\n",
    "#2、分析自变量与目标变量（质量）的相关性\n",
    "\n",
    "\n",
    "#3、通过散点图重点分析酒精含量与质量的相关性、挥发性酸与质量的相关性，可以得出什么结论？\n",
    "\n",
    "\n",
    "#4、将数据集按75%和25%的比例分成训练集和测试集，进行回归分析，并给出模型训练的性能评估\n",
    "\n",
    "\n",
    "#5、思考：该如果改进模型学习的效果？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#分类问题示例：使用逻辑回归模型对鸢尾花数据集分类，实验：使用逻辑回归模型对肿瘤数据集分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、从winequality-red.csv文件中读入输入到一个Pandas对象中，并查看数据的基本情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
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       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.075</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.9</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.069</td>\n",
       "      <td>15.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.9964</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.3</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.065</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.9946</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.47</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.073</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.36</td>\n",
       "      <td>0.57</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.5</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.36</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.071</td>\n",
       "      <td>17.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.80</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "5            7.4              0.66         0.00             1.8      0.075   \n",
       "6            7.9              0.60         0.06             1.6      0.069   \n",
       "7            7.3              0.65         0.00             1.2      0.065   \n",
       "8            7.8              0.58         0.02             2.0      0.073   \n",
       "9            7.5              0.50         0.36             6.1      0.071   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "5                 13.0                  40.0   0.9978  3.51       0.56   \n",
       "6                 15.0                  59.0   0.9964  3.30       0.46   \n",
       "7                 15.0                  21.0   0.9946  3.39       0.47   \n",
       "8                  9.0                  18.0   0.9968  3.36       0.57   \n",
       "9                 17.0                 102.0   0.9978  3.35       0.80   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  \n",
       "5      9.4        5  \n",
       "6      9.4        5  \n",
       "7     10.0        7  \n",
       "8      9.5        7  \n",
       "9     10.5        5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "winequality = pd.read_csv('winequality-red.csv',sep=';')\n",
    "winequality.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 12 columns):\n",
      "fixed acidity           1599 non-null float64\n",
      "volatile acidity        1599 non-null float64\n",
      "citric acid             1599 non-null float64\n",
      "residual sugar          1599 non-null float64\n",
      "chlorides               1599 non-null float64\n",
      "free sulfur dioxide     1599 non-null float64\n",
      "total sulfur dioxide    1599 non-null float64\n",
      "density                 1599 non-null float64\n",
      "pH                      1599 non-null float64\n",
      "sulphates               1599 non-null float64\n",
      "alcohol                 1599 non-null float64\n",
      "quality                 1599 non-null int64\n",
      "dtypes: float64(11), int64(1)\n",
      "memory usage: 150.0 KB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据的基本情况\n",
    "winequality.info()   # 查看数据的基本信息，比如数据类型和缺失值情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winequality.describe()  # 查看数据的统计摘要信息，比如平均值、最大值、最小值等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、分析自变量与目标变量（质量）的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "quality                 1.000000\n",
       "alcohol                 0.476166\n",
       "sulphates               0.251397\n",
       "citric acid             0.226373\n",
       "fixed acidity           0.124052\n",
       "residual sugar          0.013732\n",
       "free sulfur dioxide    -0.050656\n",
       "pH                     -0.057731\n",
       "chlorides              -0.128907\n",
       "density                -0.174919\n",
       "total sulfur dioxide   -0.185100\n",
       "volatile acidity       -0.390558\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算相关系数\n",
    "winequality.corr()['quality'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fixed acidity</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.256131</td>\n",
       "      <td>0.671703</td>\n",
       "      <td>0.114777</td>\n",
       "      <td>0.093705</td>\n",
       "      <td>-0.153794</td>\n",
       "      <td>-0.113181</td>\n",
       "      <td>0.668047</td>\n",
       "      <td>-0.682978</td>\n",
       "      <td>0.183006</td>\n",
       "      <td>-0.061668</td>\n",
       "      <td>0.124052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volatile acidity</th>\n",
       "      <td>-0.256131</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.552496</td>\n",
       "      <td>0.001918</td>\n",
       "      <td>0.061298</td>\n",
       "      <td>-0.010504</td>\n",
       "      <td>0.076470</td>\n",
       "      <td>0.022026</td>\n",
       "      <td>0.234937</td>\n",
       "      <td>-0.260987</td>\n",
       "      <td>-0.202288</td>\n",
       "      <td>-0.390558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citric acid</th>\n",
       "      <td>0.671703</td>\n",
       "      <td>-0.552496</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.143577</td>\n",
       "      <td>0.203823</td>\n",
       "      <td>-0.060978</td>\n",
       "      <td>0.035533</td>\n",
       "      <td>0.364947</td>\n",
       "      <td>-0.541904</td>\n",
       "      <td>0.312770</td>\n",
       "      <td>0.109903</td>\n",
       "      <td>0.226373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual sugar</th>\n",
       "      <td>0.114777</td>\n",
       "      <td>0.001918</td>\n",
       "      <td>0.143577</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055610</td>\n",
       "      <td>0.187049</td>\n",
       "      <td>0.203028</td>\n",
       "      <td>0.355283</td>\n",
       "      <td>-0.085652</td>\n",
       "      <td>0.005527</td>\n",
       "      <td>0.042075</td>\n",
       "      <td>0.013732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chlorides</th>\n",
       "      <td>0.093705</td>\n",
       "      <td>0.061298</td>\n",
       "      <td>0.203823</td>\n",
       "      <td>0.055610</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005562</td>\n",
       "      <td>0.047400</td>\n",
       "      <td>0.200632</td>\n",
       "      <td>-0.265026</td>\n",
       "      <td>0.371260</td>\n",
       "      <td>-0.221141</td>\n",
       "      <td>-0.128907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <td>-0.153794</td>\n",
       "      <td>-0.010504</td>\n",
       "      <td>-0.060978</td>\n",
       "      <td>0.187049</td>\n",
       "      <td>0.005562</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.667666</td>\n",
       "      <td>-0.021946</td>\n",
       "      <td>0.070377</td>\n",
       "      <td>0.051658</td>\n",
       "      <td>-0.069408</td>\n",
       "      <td>-0.050656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <td>-0.113181</td>\n",
       "      <td>0.076470</td>\n",
       "      <td>0.035533</td>\n",
       "      <td>0.203028</td>\n",
       "      <td>0.047400</td>\n",
       "      <td>0.667666</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.071269</td>\n",
       "      <td>-0.066495</td>\n",
       "      <td>0.042947</td>\n",
       "      <td>-0.205654</td>\n",
       "      <td>-0.185100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>0.668047</td>\n",
       "      <td>0.022026</td>\n",
       "      <td>0.364947</td>\n",
       "      <td>0.355283</td>\n",
       "      <td>0.200632</td>\n",
       "      <td>-0.021946</td>\n",
       "      <td>0.071269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.341699</td>\n",
       "      <td>0.148506</td>\n",
       "      <td>-0.496180</td>\n",
       "      <td>-0.174919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pH</th>\n",
       "      <td>-0.682978</td>\n",
       "      <td>0.234937</td>\n",
       "      <td>-0.541904</td>\n",
       "      <td>-0.085652</td>\n",
       "      <td>-0.265026</td>\n",
       "      <td>0.070377</td>\n",
       "      <td>-0.066495</td>\n",
       "      <td>-0.341699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.196648</td>\n",
       "      <td>0.205633</td>\n",
       "      <td>-0.057731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sulphates</th>\n",
       "      <td>0.183006</td>\n",
       "      <td>-0.260987</td>\n",
       "      <td>0.312770</td>\n",
       "      <td>0.005527</td>\n",
       "      <td>0.371260</td>\n",
       "      <td>0.051658</td>\n",
       "      <td>0.042947</td>\n",
       "      <td>0.148506</td>\n",
       "      <td>-0.196648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.093595</td>\n",
       "      <td>0.251397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>-0.061668</td>\n",
       "      <td>-0.202288</td>\n",
       "      <td>0.109903</td>\n",
       "      <td>0.042075</td>\n",
       "      <td>-0.221141</td>\n",
       "      <td>-0.069408</td>\n",
       "      <td>-0.205654</td>\n",
       "      <td>-0.496180</td>\n",
       "      <td>0.205633</td>\n",
       "      <td>0.093595</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.476166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>0.124052</td>\n",
       "      <td>-0.390558</td>\n",
       "      <td>0.226373</td>\n",
       "      <td>0.013732</td>\n",
       "      <td>-0.128907</td>\n",
       "      <td>-0.050656</td>\n",
       "      <td>-0.185100</td>\n",
       "      <td>-0.174919</td>\n",
       "      <td>-0.057731</td>\n",
       "      <td>0.251397</td>\n",
       "      <td>0.476166</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      fixed acidity  volatile acidity  citric acid  \\\n",
       "fixed acidity              1.000000         -0.256131     0.671703   \n",
       "volatile acidity          -0.256131          1.000000    -0.552496   \n",
       "citric acid                0.671703         -0.552496     1.000000   \n",
       "residual sugar             0.114777          0.001918     0.143577   \n",
       "chlorides                  0.093705          0.061298     0.203823   \n",
       "free sulfur dioxide       -0.153794         -0.010504    -0.060978   \n",
       "total sulfur dioxide      -0.113181          0.076470     0.035533   \n",
       "density                    0.668047          0.022026     0.364947   \n",
       "pH                        -0.682978          0.234937    -0.541904   \n",
       "sulphates                  0.183006         -0.260987     0.312770   \n",
       "alcohol                   -0.061668         -0.202288     0.109903   \n",
       "quality                    0.124052         -0.390558     0.226373   \n",
       "\n",
       "                      residual sugar  chlorides  free sulfur dioxide  \\\n",
       "fixed acidity               0.114777   0.093705            -0.153794   \n",
       "volatile acidity            0.001918   0.061298            -0.010504   \n",
       "citric acid                 0.143577   0.203823            -0.060978   \n",
       "residual sugar              1.000000   0.055610             0.187049   \n",
       "chlorides                   0.055610   1.000000             0.005562   \n",
       "free sulfur dioxide         0.187049   0.005562             1.000000   \n",
       "total sulfur dioxide        0.203028   0.047400             0.667666   \n",
       "density                     0.355283   0.200632            -0.021946   \n",
       "pH                         -0.085652  -0.265026             0.070377   \n",
       "sulphates                   0.005527   0.371260             0.051658   \n",
       "alcohol                     0.042075  -0.221141            -0.069408   \n",
       "quality                     0.013732  -0.128907            -0.050656   \n",
       "\n",
       "                      total sulfur dioxide   density        pH  sulphates  \\\n",
       "fixed acidity                    -0.113181  0.668047 -0.682978   0.183006   \n",
       "volatile acidity                  0.076470  0.022026  0.234937  -0.260987   \n",
       "citric acid                       0.035533  0.364947 -0.541904   0.312770   \n",
       "residual sugar                    0.203028  0.355283 -0.085652   0.005527   \n",
       "chlorides                         0.047400  0.200632 -0.265026   0.371260   \n",
       "free sulfur dioxide               0.667666 -0.021946  0.070377   0.051658   \n",
       "total sulfur dioxide              1.000000  0.071269 -0.066495   0.042947   \n",
       "density                           0.071269  1.000000 -0.341699   0.148506   \n",
       "pH                               -0.066495 -0.341699  1.000000  -0.196648   \n",
       "sulphates                         0.042947  0.148506 -0.196648   1.000000   \n",
       "alcohol                          -0.205654 -0.496180  0.205633   0.093595   \n",
       "quality                          -0.185100 -0.174919 -0.057731   0.251397   \n",
       "\n",
       "                       alcohol   quality  \n",
       "fixed acidity        -0.061668  0.124052  \n",
       "volatile acidity     -0.202288 -0.390558  \n",
       "citric acid           0.109903  0.226373  \n",
       "residual sugar        0.042075  0.013732  \n",
       "chlorides            -0.221141 -0.128907  \n",
       "free sulfur dioxide  -0.069408 -0.050656  \n",
       "total sulfur dioxide -0.205654 -0.185100  \n",
       "density              -0.496180 -0.174919  \n",
       "pH                    0.205633 -0.057731  \n",
       "sulphates             0.093595  0.251397  \n",
       "alcohol               1.000000  0.476166  \n",
       "quality               0.476166  1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winequality.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3、通过散点图重点分析酒精含量与质量的相关性、挥发性酸与质量的相关性，可以得出什么结论？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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REZERpwKPiIiIiIiIiMiIU4FHRERERERERGTEqcAjIiIiIiIiIjLiVOARERER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\numWIMHbUw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RERERERERHJOBV4REREREREREQyTgUeEREREREREZGMU4FHRERERERERCTj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104d17305c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置绘图布局\n",
    "fig, axs = plt.subplots(nrows=3, ncols=4, figsize=(16, 12))\n",
    "\n",
    "# 遍历每个特征并绘制散点图\n",
    "for i, column in enumerate(winequality.columns[:-1]):  # 最后一列是质量，不需要绘制\n",
    "    row = i // 4\n",
    "    col = i % 4\n",
    "    axs[row, col].scatter(winequality[column], winequality['quality'], alpha=0.5)\n",
    "    axs[row, col].set_xlabel(column)\n",
    "    axs[row, col].set_ylabel('Quality')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b3f3Ibi+0pqel4WGpry/6Oj3d3vF37JBWrZLMoq87dhTvaxYtc3ML36e1J5ei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\nhw6KYqtHmNnB8deHKZq0fFZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x104d24e5320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9v3/+vP7+ML+R/socIzI5qec4km6fVm7dcu1aLWdcU1NTTe8zikrH8PHxpg9V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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104d3f93e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 绘制酒精含量与质量的散点图\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(winequality['alcohol'], winequality['quality'], color='blue')\n",
    "plt.title('Scatter Plot of Alcohol Content vs Quality')\n",
    "plt.xlabel('Alcohol Content')\n",
    "plt.ylabel('Quality')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 绘制挥发性酸与质量的散点图\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(winequality['volatile acidity'], winequality['quality'], color='red')\n",
    "plt.title('Scatter Plot of Volatile Acidity vs Quality')\n",
    "plt.xlabel('Volatile Acidity')\n",
    "plt.ylabel('Quality')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rkx9h7zF7B4hI6oWKLRERkRLM+808LrznwqLb+Rn1849pUh0qtkRERApQykT2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\nV2MwszuAMcBG4CvufmuNQ6pLZnY1MJ3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x104d60f8a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ch197OGP5vrMSR7mUfImIiHQOkSZbZjYQuAbYF3DgDHf/a5RtdkdmxkOnP9Q2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104d4fa99e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用线性回归拟合酒精含量与质量的数据\n",
    "alcohol = winequality['alcohol'].values.reshape(-1, 1)\n",
    "quality = winequality['quality'].values.reshape(-1, 1)\n",
    "reg_alcohol = LinearRegression().fit(alcohol, quality)\n",
    "\n",
    "# 绘制酒精含量与质量的散点图和拟合曲线\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(winequality['alcohol'], winequality['quality'], color='blue', label='Data')\n",
    "plt.plot(alcohol, reg_alcohol.predict(alcohol),color='green',linewidth=2, label='Fit')\n",
    "plt.title('Scatter Plot of Alcohol Content vs Quality with Fit Line')\n",
    "plt.xlabel('Alcohol Content')\n",
    "plt.ylabel('Quality')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 使用线性回归拟合挥发性酸与质量的数据\n",
    "volatile_acidity = winequality['volatile acidity'].values.reshape(-1, 1)\n",
    "reg_volatile_acidity = LinearRegression().fit(volatile_acidity, quality)\n",
    "\n",
    "# 绘制挥发性酸与质量的散点图和拟合曲线\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(winequality['volatile acidity'], winequality['quality'], color='red', label='Data')\n",
    "plt.plot(volatile_acidity, reg_volatile_acidity.predict(volatile_acidity),color='green', linewidth=2, label='Fit')\n",
    "plt.title('Scatter Plot of Volatile Acidity vs Quality with Fit Line')\n",
    "plt.xlabel('Volatile Acidity')\n",
    "plt.ylabel('Quality')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 酒精含量与质量的相关性：从散点图中可以看出，酒精含量似乎与质量呈正相关关系，即酒精含量较高的葡萄酒往往具有较高的质量评分。\n",
    "\n",
    "# 挥发性酸与质量的相关性：在挥发性酸与质量的散点图中，可能存在一个负相关趋势，即挥发性酸含量较低的葡萄酒可能具有较高的质量评分。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、将数据集按75%和25%的比例分成训练集和测试集，进行回归分析，并给出模型训练的性能评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差(MSE): 0.422164634273\n",
      "R^2分数: 0.317541384353\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jd83WvUDpKvcXptpERDJLRQWMGBGuLPzd70Jh0iOOWHlciZaI1LJ0ky1zX3lR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\nSKqUY0sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f499554d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 加载数据集\n",
    "winequality = pd.read_csv('winequality-red.csv',sep=';')\n",
    "\n",
    "# 指定自变量（特征变量）和因变量（目标变量）\n",
    "X = winequality[['alcohol', 'volatile acidity']]\n",
    "y = winequality['quality']\n",
    "\n",
    "# 将数据集分割成训练集和测试集（75%训练集，25%测试集）\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "\n",
    "# 初始化线性回归模型\n",
    "model = LinearRegression()\n",
    "\n",
    "# 在训练集上训练模型\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# 计算均方误差（Mean Squared Error）\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "# 计算R^2分数\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "# 打印性能评估结果\n",
    "print(\"均方误差(MSE):\", mse)\n",
    "print(\"R^2分数:\", r2)\n",
    "\n",
    "# 绘制预测值与实际值的散点图\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], '--', color='red')\n",
    "plt.title('Actual vs Predicted')\n",
    "plt.xlabel('Actual')\n",
    "plt.ylabel('Predicted')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 均方误差（Mean Squared Error，MSE）：该值衡量模型预测值与实际观测值之间的平方差的平均值。MSE值越低，表示模型的预测性能越好。\n",
    "\n",
    "# R^2分数：R^2分数是拟合优度的一种度量，表示模型对目标变量方差的解释程度。该值范围从0到1，越接近1表示模型拟合得越好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For alcohol content vs quality:\n",
      "Mean Squared Error (MSE): 0.500440569178\n",
      "R^2 Score: 0.19100286872\n",
      "\n",
      "For volatile acidity vs quality:\n",
      "Mean Squared Error (MSE): 0.502715093691\n",
      "R^2 Score: 0.18732594099\n"
     ]
    },
    {
     "data": {
      "image/png": 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ERCRENERQREREREQkRBKO/ZDyc8opp7gmTZr4HYaIiPjg3Xff3emcq3/sR/pHeUpEJHaV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\nMlxDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f498c55e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 加载数据集\n",
    "winequality = pd.read_csv('winequality-red.csv', sep=';')\n",
    "\n",
    "# 提取特征和标签\n",
    "X_alcohol = winequality['alcohol'].values.reshape(-1, 1)\n",
    "X_volatile_acidity = winequality['volatile acidity'].values.reshape(-1, 1)\n",
    "y = winequality['quality']\n",
    "\n",
    "# 将数据集按75%和25%的比例分成训练集和测试集\n",
    "X_alcohol_train, X_alcohol_test, y_train, y_test = train_test_split(X_alcohol, y, test_size=0.25, random_state=42)\n",
    "X_volatile_acidity_train, X_volatile_acidity_test, _, _ = train_test_split(X_volatile_acidity, y, test_size=0.25, random_state=42)\n",
    "\n",
    "# 创建线性回归模型并在训练集上进行训练\n",
    "reg_alcohol = LinearRegression().fit(X_alcohol_train, y_train)\n",
    "reg_volatile_acidity = LinearRegression().fit(X_volatile_acidity_train, y_train)\n",
    "\n",
    "# 在测试集上进行预测\n",
    "y_pred_alcohol = reg_alcohol.predict(X_alcohol_test)\n",
    "y_pred_volatile_acidity = reg_volatile_acidity.predict(X_volatile_acidity_test)\n",
    "\n",
    "# 计算性能评估指标\n",
    "mse_alcohol = mean_squared_error(y_test, y_pred_alcohol)\n",
    "mse_volatile_acidity = mean_squared_error(y_test, y_pred_volatile_acidity)\n",
    "r2_alcohol = r2_score(y_test, y_pred_alcohol)\n",
    "r2_volatile_acidity = r2_score(y_test, y_pred_volatile_acidity)\n",
    "\n",
    "print(\"For alcohol content vs quality:\")\n",
    "print(\"Mean Squared Error (MSE):\", mse_alcohol)\n",
    "print(\"R^2 Score:\", r2_alcohol)\n",
    "print()\n",
    "print(\"For volatile acidity vs quality:\")\n",
    "print(\"Mean Squared Error (MSE):\", mse_volatile_acidity)\n",
    "print(\"R^2 Score:\", r2_volatile_acidity)\n",
    "\n",
    "\n",
    "# 绘制预测值与实际值的散点图\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# 绘制酒精含量与质量的散点图\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(y_test, y_pred_alcohol, color='blue')\n",
    "plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=2)\n",
    "plt.xlabel('Actual Quality')\n",
    "plt.ylabel('Predicted Quality')\n",
    "plt.title('Alcohol Content vs Quality')\n",
    "\n",
    "# 绘制挥发性酸与质量的散点图\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(y_test, y_pred_volatile_acidity, color='red')\n",
    "plt.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=2)\n",
    "plt.xlabel('Actual Quality')\n",
    "plt.ylabel('Predicted Quality')\n",
    "plt.title('Volatile Acidity vs Quality')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5、思考：该如果改进模型学习的效果？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#有几种方法可以尝试改进模型学习的效果：\n",
    "\n",
    "#（1）特征工程：尝试添加新的特征或进行特征转换，以提供更多信息给模型。您可以尝试创建交互特征，多项式特征或使用特征选择技术来选择最相关的特征。\n",
    "\n",
    "#（2）模型选择：除了线性回归模型，您可以尝试其他类型的模型，如决策树、随机森林、支持向量机等。不同类型的模型可能会对数据的特性有不同的适应性。\n",
    "\n",
    "#（3）调整超参数：如果您选择了一个复杂的模型，如随机森林或支持向量机，您可以尝试调整其超参数来优化模型的性能。可以使用交叉验证来选择最佳的超参数组合。\n",
    "\n",
    "#（4）集成学习：尝试使用集成学习技术，如Bagging、Boosting或Stacking，将多个模型的预测结果结合起来，以提高整体性能。\n",
    "\n",
    "#（5）处理异常值和缺失值：检测和处理异常值，以及填补缺失值，有助于提高模型的稳健性和泛化能力。\n",
    "\n",
    "#（6）正则化：对于线性模型，可以尝试使用L1正则化（Lasso）或L2正则化（Ridge）来防止过拟合，并提高模型的泛化能力。\n",
    "\n",
    "#（7）集成特征选择：使用特征选择技术来筛选出最具预测性的特征，可以提高模型的效果，并减少模型的复杂性。\n",
    "\n",
    "#（8）数据增强：对于较小的数据集，可以尝试使用数据增强技术来生成额外的训练样本，以增加模型的泛化能力。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "交叉验证5-折得分: [ 0.36074616  0.21523087  0.26206233  0.31444382  0.42258887]\n",
      "均方误差(MSE): 0.32\n",
      "R^2分数: 0.07\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 加载数据集\n",
    "winequality = pd.read_csv('winequality-red.csv', sep=';')\n",
    "\n",
    "# 准备自变量(特征值)和目标变量\n",
    "X = winequality[['alcohol', 'volatile acidity']]\n",
    "y = winequality['quality']\n",
    "\n",
    "# 初始化线性回归模型\n",
    "model = LinearRegression()\n",
    "\n",
    "# 定义K折交叉验证的折数\n",
    "k = 5\n",
    "\n",
    "# 初始化KFold对象\n",
    "kf = KFold(n_splits=k, shuffle=True, random_state=42)\n",
    "\n",
    "# 执行交叉验证\n",
    "cv_scores = cross_val_score(model, X, y, cv=kf)\n",
    "\n",
    "# 计算并打印平均得分和标准差\n",
    "mean_score = cv_scores.mean()\n",
    "std_score = cv_scores.std()\n",
    "print(f\"交叉验证{k}-折得分: {cv_scores}\")\n",
    "print(f\"均方误差(MSE): {mean_score:.2f}\")\n",
    "print(f\"R^2分数: {std_score:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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